import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey




class 					_UpperCAmelCase			:



 '''simple docstring'''




 def __init__(		self				:   Any							, lowercase_				:   Any							, lowercase_				:   int							, lowercase_				:   int)						->	Optional[int]:


   """simple docstring"""







   if dst_width < 0 or dst_height < 0:
     raise ValueError("Destination width/height should be > 0")

   _UpperCamelCase           =    img
   _UpperCamelCase           =    img.shape[1]
   _UpperCamelCase           =    img.shape[0]
   _UpperCamelCase           =    dst_width
   _UpperCamelCase           =    dst_height

   _UpperCamelCase           =    self.src_w / self.dst_w
   _UpperCamelCase           =    self.src_h / self.dst_h

   _UpperCamelCase           =    _UpperCamelCase           =    (
       np.ones((self.dst_h, self.dst_w, 3)							, np.uinta) * 255
   )







 def 		__UpperCAmelCase				(		self				:   Any)						->	Union[str, Any]:


   """simple docstring"""







   for i in range(self.dst_h):
     for j in range(self.dst_w):
       _UpperCamelCase           =    self.img[self.get_y(lowercase_)][self.get_x(lowercase_)]







 def 		__UpperCAmelCase				(		self				:   Optional[int]							, lowercase_				:   int)						->	int:


   """simple docstring"""







   return int(self.ratio_x * x)







 def 		__UpperCAmelCase				(		self				:   Optional[int]							, lowercase_				:   int)						->	int:


   """simple docstring"""







   return int(self.ratio_y * y)


if __name__ == "__main__":
   lowerCamelCase__,lowerCamelCase__  =					800, 600
   lowerCamelCase__  =					imread('''image_data/lena.jpg''', 1)
   lowerCamelCase__  =					NearestNeighbour(im, dst_w, dst_h)
   n.process()

   imshow(
       F"Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}", n.output
   )
   waitKey(0)
   destroyAllWindows()





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase__  =					{
    '''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
    '''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
    '''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
    '''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''Wav2Vec2ForAudioFrameClassification''',
       '''Wav2Vec2ForCTC''',
       '''Wav2Vec2ForMaskedLM''',
       '''Wav2Vec2ForPreTraining''',
       '''Wav2Vec2ForSequenceClassification''',
       '''Wav2Vec2ForXVector''',
       '''Wav2Vec2Model''',
       '''Wav2Vec2PreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFWav2Vec2ForCTC''',
       '''TFWav2Vec2Model''',
       '''TFWav2Vec2PreTrainedModel''',
       '''TFWav2Vec2ForSequenceClassification''',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''FlaxWav2Vec2ForCTC''',
       '''FlaxWav2Vec2ForPreTraining''',
       '''FlaxWav2Vec2Model''',
       '''FlaxWav2Vec2PreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
   from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
   from .processing_wavaveca import WavaVecaProcessor
   from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_wavaveca import (
          WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
          WavaVecaForAudioFrameClassification,
          WavaVecaForCTC,
          WavaVecaForMaskedLM,
          WavaVecaForPreTraining,
          WavaVecaForSequenceClassification,
          WavaVecaForXVector,
          WavaVecaModel,
          WavaVecaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_wavaveca import (
          TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFWavaVecaForCTC,
          TFWavaVecaForSequenceClassification,
          TFWavaVecaModel,
          TFWavaVecaPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_wavaveca import (
          FlaxWavaVecaForCTC,
          FlaxWavaVecaForPreTraining,
          FlaxWavaVecaModel,
          FlaxWavaVecaPreTrainedModel,
      )


else:
   import sys

   lowerCamelCase__  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




